1. 1 UNIT 13: DATA ANALYSIS

2. 2 A. Editing, Coding and Computer Entry Editing in field i.e after completion of each interview/questionnaire. Editing again for any errors not detected in the field.

3. 3 Coding of questionnaires i.e. converting data to numerical codes. This will allow for statistical analysis. Entering data into the computer. Research variables and their numeric values are entered into the computer.

4. 4 B. Research Variables To carry out statistical analysis, concepts have to be operationalized. These concepts have to be defined as variables. It is important to explicitly set out the variables of your study and how they will be measured. In any one study, some variables will be nominal, ordinal, interval and ratio.

5. 5 The level at which variables are measured is a very important determinant of how the research data will be analyzed i.e. statistical analysis techniques. It is useful to identify research variables at the proposal preparation stage

6. 6 C. Descriptive Statistics Summarizing data using descriptive statistics. This enables a researcher to describe the distribution of the research variables using a few indices or statistics. Descriptive statistics give a good quick picture of how the variables behaved i.e. their distribution.

7. 7 In starting your analysis, compute descriptive statistics for all your variables. This will serve as yet another quality check on your data. Descriptive statistics are of various types -Measures of central tendency Mode, median, mean.

8. 8 -Measures of variability: This is the dispersion of measures around the central score e.g. the mean. Typical measures of variability are the range and standard deviation.

9. 9 -Frequency distribution: This gives a picture of the shape of a variable i.e. how it is distributed. *Frequency tables *Histograms * Frequency polygrams *Bar charts

10. 10 -Percentages: Proportions of sub-groups to the total group. They range from 0% to 100%. Percentages are important where there is need to compare groups that differ in size.

11. 11 D. Relationships There are statistics that measure the relationships between two or more variables. Correlation co-efficient Co-movement of variables

12. 12 Chi-square statistics dependence or independence of variables. Regression co-efficient Determining an equation that explains how variables are related.

13. 13 E. Inferential Statistics The ultimate purpose of research is to be able to generalize the results from samples to populations. Hypothesis testing techniques are used to generalize from the sample to the population. Generalizing from a sample to a population is statistical inference.

14. 14 Commonly used statistical procedures in statistical inference include correlation, regression (simple and multiple), chi-square test and analysis of variance (for experimental data).